• 한국군사과학기술학회지
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• 제22권2호
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• pp.189-196
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• 2019

The inertial sensor errors in SDINS(Strapdown Inertial Navigation System) can be compensated by rotating the inertial measurement unit and it is called RINS(Rotational Inertial Navigation System). It is assumed that the error of the inertial sensor in RINS is a static bias. However, the error of the inertial sensor actually developed and produced is not a static bias due to the change of the temperature applied to the sensor and the influence of the earth's gravity acceleration. In this paper, we propose a six-position gyro bias calibration method to evaluate the gyro bias required for RINS and present the test results of applying it to a ring laser gyro inertial navigation system under development.

• 한국항공우주학회지
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• 제50권9호
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• pp.617-625
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• 2022

본 논문은 사격 차선 정렬을 위하여 움직일 수 있는 교정 대상을 이용해 각속도계와 가속도계의 편차를 보상하는 방법을 다룬다. 교정 대상에 대한 정보는 영상 센서를 통해 획득하며 이를 이용해 발사장치에 부착된 관성측정 장치의 오차를 보정한다. 시뮬레이션을 통해 제안한 알고리즘의 성능을 검증하였으며, 특히 관성 좌표계에서 교정 대상에 대한 위치 정보를 정확하게 획득함으로써 발사장치의 관성 센서 편차를 효과적으로 보상할 수 있음을 보인다.

• International Journal of Control, Automation, and Systems
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• 제2권4호
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• pp.536-541
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• 2004

It is important to estimate and calibrate sensor errors in maintaining the performance level of SDINS. In this study, an estimation technique of fixed sensor errors for SDINS calibration is discussed. First, the fixed errors of gyros and accelerometers, excluding gyro biases are estimated by the navigation information of SDINS in multi-position. The SDINS with RLG includes flexure errors. In this study, the gyros flexures are out of consideration, but the proposed procedure selects certain positions and rotations in order to minimize the influence of flexures. Secondly, the influences of random walks, flexures and orientation errors are verified via numerical simulations. Thirdly, applying the previous estimated errors to SDINS, the estimation of gyro biases is conducted via the additional control signals of close-loop self-alignment. Lastly, the experiments illustrate that the extracted calibration parameters are available for the improvement of SDINS.

• International Journal of Aeronautical and Space Sciences
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• 제11권3호
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• pp.234-239
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• 2010

The utilization of micro-electro-mechanical system (MEMS) gyros and accelerometers in low-level inertial measurement unit (IMU) influences cost effectiveness in a positive way under the condition that device error characteristics are fully calibrated. The conventional calibration process utilizes a rate table; however, this paper proposes a new method for achieving reference calibration data from the natural motion of pendulum to which the IMU undergoing calibration is attached. This concept was validated with experimental data. The pendulum angle measurements correlate extremely well with the solutions acquired from the pendulum equation of motion. The calibration data were computed using the regression method. The whole process was validated by comparing the measurement from the 6 sensor components with the measurements reconstructed using the identified calibration data.

• 한국컴퓨터그래픽스학회논문지
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• 제19권4호
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• pp.13-22
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• 2013

모션 캡처 장치는 자연스러운 인체 동작을 생성하는 것을 용이하게 하여 영화, 컴퓨터 게임, 컴퓨터 애니메이션 등 여러 분야에서 폭넓게 사용되고 있다. 그 중 관성 센서를 활용한 모션 캡처 장치는 보다 널리 사용되고 있는 광학 모션 캡처 장비에 비해 소요 공간과 비용 측면에서 이점을 가지고 있으나 비교적 높은 노이즈로 인해 측정 결과의 정밀도가 떨어지는 단점이 있다. 특히 관성 센서에 포함되어 중력 방향을 계측하는 가속도 센서는 센서의 선형 가속 운동으로 인해 중력 방향의 계측 정밀도가 떨어지는 문제를 갖는다. 본 논문에서는 관절체에 부착된 센서의 자세 측정 정확도를 높이기 위해 가속도 센서에서 선형 가속도 성분을 제거하는 기법을 제안한다. 아울러 센서가 부착되어 있는 관절체의 회전축 및 센서의 부착 위치를 보정하는 기법을 소개한다. 이 보정 기법은 관성 센서가 관절체의 임의의 위치와 방향으로 부착되는 것을 가능하게 한다.

• 한국정보통신학회논문지
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• 제12권10호
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• pp.1822-1829
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• 2008

항체가 정밀한 항법 정보를 얻기 위해서는 관성 센서의 초기 오차에 대한 보정이 매우 중요하다. 본 논문에서는 레이트 테이블과 고성능 센서를 기준 센서로 사용하여, 저급 MEMS 관성 센서 오차 보정에 따르는 비용과 효율성의 어려움을 극복하는 방법을 제안하였다. 초기 오차 보정 과정에서 기준 센서와 타겟 센서에 같은 동적 입력을 인가한 후 결과를 분석하였다. 실험 결과를 통해 제안된 초기 오차 보정 방법이 실제로 매우 효율적이며 유용함을 확인할 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1679-1683
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• 1997

In this paper a linear Kalman filter for calibration of gimballed inertial navigation system(GINS) is designed and its performace is analyzed through the simulation with a real navigation data. Simulation results show that the proposed Kalman filter gives a good performance to calibrate the sensor errors.

• 제어로봇시스템학회논문지
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• 제18권7호
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• pp.632-637
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• 2012

This paper introduces a simple and efficient calibration method for three-axis accelerometers and three-axis gyroscopes using three-axis motion rate table. Usually, the performance of low cost MEMS-based inertial sensors is affected by scale and bias errors significantly. The calibration of these errors is a bothersome problem, but the previous calibration methods cannot propose simple and efficient method to calibrate the errors of three-axis inertial sensors. This paper introduces a new simple and efficient method for the calibration of accelerometer and gyroscope. By using a three-axis motion rate table, this method can calibrate the accelerometer and gyroscope simultaneously and simply. Experimental results confirm the performance of the proposed method.

• International Journal of Precision Engineering and Manufacturing
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• 제9권3호
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• pp.46-50
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• 2008

The present status and future prospects of the levitation mass method (LMM), a technique for precision mass and force measurement, are reviewed. In the LMM, the inertial force of a mass levitated using a pneumatic linear bearing is used as the reference force applied to the objects being tested, such as force transducers, materials, or structures. The inertial force of the levitated mass is measured using an optical interferometer. We have modified this technique for dynamic force calibration of impact, oscillation, and step loads. We have also applied the LMM to material testing, providing methods for evaluating material viscoelasticity under an oscillating or impact load, evaluating material friction, evaluating the biomechanics of a human hand, and generating and measuring micro-Newton-level forces.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1846-1856
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• 2016

This study proposes a novel feature point initialization method in order to improve the accuracy of feature point positions by fusing a vision sensor and a lidar. The initialization is a process that determines three dimensional positions of feature points through two dimensional image data, which has a direct influence on performance of a 6-DoF bearing-only SLAM. Prior to the initialization, an extrinsic calibration method which estimates rotational and translational relationships between a vision sensor and lidar using multiple calibration tools was employed, then the feature point initialization method based on the estimated extrinsic calibration parameters was presented. In this process, in order to improve performance of the accuracy of the initialized feature points, an iterative automatic scaling parameter tuning technique was presented. The validity of the proposed feature point initialization method was verified in a 6-DoF bearing-only SLAM framework through an indoor and outdoor tests that compare estimation performance with the previous initialization method.